As a Senior Data Engineer, you will be responsible for designing, building, and optimizing scalable data pipelines that power AI and analytics solutions for clients.
You will work on the architecture and deployment of data infrastructure, ensuring efficiency, reliability, and security across large-scale datasets.
The position allows for 100% remote work if you are currently living in LATAM, or you can join the office in Montevideo, Uruguay.
Your tasks will include designing, developing, and maintaining scalable ETL/ELT data pipelines for AI and analytics applications.
You will optimize data architectures and storage solutions using Databricks, Snowflake, and cloud-based platforms.
You will develop big data processing jobs using PySpark, Spark SQL, and distributed computing frameworks.
Ensuring data quality, governance, and security best practices across all environments will be part of your responsibilities.
You will implement CI/CD workflows for automated deployment and infrastructure as code (IaC).
Collaboration with cross-functional teams (data scientists, software engineers, analysts) to build end-to-end data solutions will be essential.
You will lead troubleshooting and performance tuning efforts for high-volume data processing systems.
Developing and maintaining Python-based backend services to support data infrastructure will be required.
You will implement Apache Airflow, Dataplane, or similar orchestration tools to automate and monitor workflows.
Requirements:
A strong proficiency in SQL for data processing and transformation is required.
You must have strong knowledge of object-oriented programming in at least one language (Python, Scala, or Java).
Hands-on experience deploying and managing large-scale data pipelines in production environments is necessary.
Expertise in workflow orchestration tools like Apache Airflow, Dataplane, or equivalent is required.
A deep understanding of cloud-based data platforms such as Databricks and Snowflake (Databricks preferred) is essential.
Knowledge of CI/CD pipelines and infrastructure as code for data workflows is required.
Familiarity with cloud environments (AWS preferred, Azure, or GCP) and cloud-native data processing is necessary.
Expertise in Spark, PySpark, and Spark SQL, with a solid understanding of distributed computing frameworks is required.
You must have a proven ability to lead projects and mentor junior engineers in a fast-paced, collaborative environment.
Excellent written and verbal English for clear and effective communication is a must.
A minimum of 4+ years of experience working as a Data Engineer or in related positions is required.
Benefits:
You will have access to learning opportunities, including certifications in AWS, Databricks, and Snowflake.
There will be access to AI learning paths to stay up to date with the latest technologies.
Study plans, courses, and additional certifications tailored to your role will be provided.
English lessons will be offered to support your professional communication.
Career development plans and mentorship programs will help shape your career path.
You will receive anniversary and birthday gifts as part of the company culture.
Company-provided equipment for remote work will be available.
Other benefits may vary according to your location in LATAM.
